IRSYAD, ZHAFARI (2024) PENERAPAN ALGORITMA DECISION TREE UNTUK KLASIFIKASI KELAYAKAN PESERTA BOOTCAMP UNTUK DISALURKAN KE JOBSEEKER. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
Online bootcamps are growing in popularity as a flexible alternative and can be accessed from anywhere. However, participants' success in completing the online bootcamp program is influenced by several factors. This research aims to analyze the suitability of participants by utilizing a Decision Tree. Using a dataset of 4016 data from the company PT Mega Harapan Mulia and 7 attributes in the form of name, bootcamp attended, pretest scores, miniquiz scores, posttest scores, project scores, and the value of the time needed to complete the bootcamp. Researchers carried out three test scenarios with varying ratios of training and test data to produce a Decision Tree model that provided high accuracy, reaching 98%, 99% and 99% respectively. The evaluation results show that the “processing time” feature is consistently considered the most important in decision making. Decision tree visualization and confusion matrix analysis provide insight into the model's performance in predicting "Eligible" or "Not Eligible" classes based on participant characteristics. This study contributes to increasing understanding of the factors that influence the eligibility of online bootcamp participants. Keywords: Decision Tree Method, CART Algorithm, Bootcamp Online Bootcamp online semakin populer sebagai alternatif fleksibel dan dapat diakses dari mana saja. Namun, keberhasilan peserta dalam menyelesaikan program bootcamp online dipengaruhi oleh beberapa faktor. Penelitian ini bertujuan menganalisis kelayakan peserta dengan memanfaatkan Decision Tree. Menggunakan dataset berjumlah 4016 data dari perusahaan PT Mega Harapan Mulia dan 7 atribut berupa nama, bootcamp yang diikuti, nilai pretest, nilai miniquiz, nilai posttest, nilai project, dan nilai waktu yang dibutuhkan untuk menyelesaikan bootcamp. Peneliti melakukan tiga skenario pengujian dengan variasi rasio data pelatihan dan pengujian menghasilkan model Decision Tree yang memberikan akurasi tinggi, mencapai 98%, 99%, dan 98% secara berturut-turut. Hasil evaluasi menunjukkan bahwa fitur “waktu pengerjaan” konsisten dianggap paling penting dalam pengambilan keputusan. Visualisasi pohon keputusan dan analisis confusion matrix memberikan wawasan tentang kinerja model dalam memprediksi kelas "Layak" atau "Tidak Layak" berdasarkan karakteristik peserta. Studi ini memberikan kontribusi dalam meningkatkan pemahaman terhadap faktor-faktor yang memengaruhi kelayakan peserta bootcamp online. Kata Kunci: Metode Decision Tree, Algoritma CART, Kamp Pelatihan Online.
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